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Model-based Safe Deep Reinforcement Learning and Empirical Analysis of Safety via Attribution
During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents
perform a significant number of random exploratory steps, which in the real-world limit the
practicality of these algorithms ...
Novel Reinforcement Learning Algorithms and Applications to Hybrid Control Design Problems
The thesis is a compilation of two independent works.
In the first work, we develop novel weight assignment procedure, which helps us develop several schedule based algorithms. Learning the value function of a given policy ...
Average Reward Actor-Critic with Deterministic Policy Search
The average reward criterion is relatively less studied as most existing works in the Reinforcement Learning literature consider the discounted reward criterion. There are few recent works that present on-policy average ...
Stochastic Optimization And Its Application In Reinforcement Learning
Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based ...
Algorithms for various cost criteria in Reinforcement Learning
In this thesis we will look at various Reinforcement Learning algorithms. We will look at algorithms for various cost criteria or reward objectives namely Finite Horizon, Discounted Cost, Risk-Sensitive Cost. For Finite ...
Barrier Function Inspired Reward Shaping in Reinforcement Learning
Reinforcement Learning (RL) has progressed from simple control tasks to complex real-world challenges with large state spaces. During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents ...
Single and Multi-Agent Finite Horizon Reinforcement Learning Algorithms for Smart Grids
In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ...
IEDFuRL: A Black-box Fuzz Tester for IEC61850-based Intelligent Electronic Devices using Reinforcement Learning
Intelligent Electronic Devices (IEDs) are essential components of modern power grids, functioning as microprocessor-based controllers that facilitate communication, monitoring, protection, and control within Supervisory ...
Resource Allocation for Sequential Decision Making Under Uncertainaty : Studies in Vehicular Traffic Control, Service Systems, Sensor Networks and Mechanism Design
(2017-11-27)
A fundamental question in a sequential decision making setting under uncertainty is “how to allocate resources amongst competing entities so as to maximize the rewards accumulated in the long run?”. The resources allocated ...
Feature Adaptation Algorithms for Reinforcement Learning with Applications to Wireless Sensor Networks And Road Traffic Control
(2017-09-20)
Many sequential decision making problems under uncertainty arising in engineering, science and economics are often modelled as Markov Decision Processes (MDPs). In the setting of MDPs, the goal is to and a state dependent ...